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» Kernels and Regularization on Graphs
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TIP
2008
213views more  TIP 2008»
13 years 8 months ago
Deblurring Using Regularized Locally Adaptive Kernel Regression
Kernel regression is an effective tool for a variety of image processing tasks such as denoising and interpolation [1]. In this paper, we extend the use of kernel regression for de...
Hiroyuki Takeda, Sina Farsiu, Peyman Milanfar
CSDA
2007
128views more  CSDA 2007»
13 years 9 months ago
Regularized linear and kernel redundancy analysis
Redundancy analysis (RA) is a versatile technique used to predict multivariate criterion variables from multivariate predictor variables. The reduced-rank feature of RA captures r...
Yoshio Takane, Heungsun Hwang
ICPR
2010
IEEE
14 years 1 months ago
Localized Multiple Kernel Regression
Multiple kernel learning (MKL) uses a weighted combination of kernels where the weight of each kernel is optimized during training. However, MKL assigns the same weight to a kerne...
Mehmet Gönen, Ethem Alpaydin
CORR
2010
Springer
103views Education» more  CORR 2010»
13 years 10 months ago
Probabilistic regular graphs
Deterministic graph grammars generate regular graphs, that form a structural extension of configuration graphs of pushdown systems. In this paper, we study a probabilistic extensio...
Nathalie Bertrand, Christophe Morvan
COMBINATORICS
2004
49views more  COMBINATORICS 2004»
13 years 9 months ago
On Regular Factors in Regular Graphs with Small Radius
Arne Hoffmann, Lutz Volkmann